Image tamper detection techniques have been proposed to address the potential tampering problems of image content, as digital images can be easily modified, diffused, transformed and duplicated via the Internet. In order to achieve high detection successful rate and self-recovery accuracy in image tamper areas, an efficient scheme of rehashing model based digital watermarking for image tamper detection and self-recovery is proposed in this study. The rehashing model consists of a series of random hash functions, which is presented to avoid numerous random number collisions that embrace the authentication data for image tamper detection and self-recovery. Additionally, these authentication data can be further embedded into the original image as digital watermarking by means of the constructed two-dimensional reference matrix. The experimental results demonstrate that this novel scheme outperforms the other related works in terms of tamper area localization and self-recovery accuracy; moreover, the proposed image tamper detection and self-recovery technique can obtain an acceptable recovery image visual quality with half image content tamper.
To improve object recognition capabilities in applications, we used orthogonal design (OD) to choose a group of optimal parameters in the parameter space of scale invariant feature transform (SIFT). In the case of global optimization (GOP) and local optimization (LOP) objectives, our aim is to show the operation of OD on the SIFT method. The GOP aims to increase the number of correctly detected true matches (NoCDTM) and the ratio of NoCDTM to all matches. In contrast, the LOP mainly aims to increase the performance of recall–precision. In detail, we first abstracted the SIFT method to a 9-way fixed-effect model with an interaction. Second, we designed a mixed orthogonal array, MA(64,23420,2), and its header table to optimize the SIFT parameters. Finally, two groups of parameters were obtained for GOP and LOP after orthogonal experiments and statistical analyses were implemented. Our experiments on four groups of data demonstrate that compared with the state-of-the-art methods, GOP can access more correct matches and is more effective against object recognition. In addition, LOP is favorable in terms of the recall–precision.
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